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Related Experiment Video

Updated: May 14, 2026

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Regression forests for efficient anatomy detection and localization in computed tomography scans.

A Criminisi¹, D Robertson, E Konukoglu

¹Microsoft Research Ltd., Cambridge, UK.

Medical Image Analysis

|February 16, 2013

Summary

This study introduces an efficient algorithm for detecting anatomical structures in CT scans using random regression forests. The method accurately maps voxels to organ location and size, outperforming existing techniques.

Keywords:

Anatomy detection Anatomy localization Random forests Regression forests

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Accurate detection and localization of anatomical structures in medical imaging are crucial for various clinical applications.
Existing methods for anatomical structure detection in computed tomography (CT) scans have limitations in efficiency and accuracy.

Purpose of the Study:

To propose a novel algorithm for efficient, automatic detection and localization of multiple anatomical structures in 3D CT scans.
To enable applications such as selective image retrieval, semantic navigation, and radiation dose tracking.

Main Methods:

A new continuous parametrization of the anatomy localization problem is introduced.
Multi-class random regression forests are employed to predict continuous, multi-variate outputs for organ location and size.
A probabilistic algorithm enables direct mapping from voxels to organ location and size in a single pass.

Main Results:

The proposed method demonstrates higher accuracy and robustness compared to multi-atlas registration and template-based nearest-neighbor detection.
Quantitative validation on 400 diverse CT scans confirms the algorithm's effectiveness.
The algorithm achieves typical run-times of approximately 4 seconds on a single-core machine.

Conclusions:

The developed algorithm offers an efficient and accurate solution for anatomical structure detection in CT scans.
The use of random regression forests provides a powerful framework for continuous, multi-variate prediction in medical imaging.
This approach has significant potential for improving clinical workflows and image analysis in radiology.